Mitigating safety risks and ensuring accountability in AI virtual agents and immersive VR applications through inclusive design and stakeholder engagement in healthcare

AI virtual agents (VAs) are computer programs that can talk like humans and do tasks such as answering patients’ questions, reminding them to follow treatments, scheduling appointments, and giving health advice. Immersive VR applications create 3D spaces that help with therapies like rehabilitation, exposure therapy, or teaching patients.

Many healthcare places use AI tools to give medical help to people who live far away or do not have many services. During the COVID-19 pandemic, virtual healthcare tools helped reduce infection risks and made sure care kept going without physical visits.

Even with these benefits, using virtual agents and VR in healthcare raises questions about ethics, legal rules, and how to keep patients safe. These issues can affect how well organizations work and their risks if things go wrong.

Key Safety and Accountability Challenges

• Patient Safety Concerns
  AI virtual agents and VR must give accurate and reliable answers. Wrong advice or mistakes could harm patients, especially if no doctor is watching. Keeping patients safe means checking AI answers often, watching how systems work, and having ways for humans to step in when needed.
• Legal Accountability
  In the U.S., it is hard to say who is responsible if AI tools make errors that hurt patients. There are questions about whether AI needs special approval to be used, and who is at fault if harm happens. This is even more complicated when patients and doctors are in different states with different laws.
• Privacy and Data Protection
  AI virtual agents collect sensitive patient information. VR may also capture body language or other personal data. Following laws like HIPAA is required. Healthcare groups must keep data very safe to avoid leaks or misuse.
• Impact on Doctor-Patient Relationship
  AI helps with routine tasks, but it means patients and doctors meet less face-to-face. This can reduce trust, care, and good communication, which might affect how patients follow treatment instructions.
• Bias and Fairness
  AI systems can show biases from the data they learn from. This could cause unfair treatment or unequal access. This is important in the U.S. where there are differences in healthcare by race, money, and location.

Inclusive Design and Stakeholder Engagement as a Strategy

Research shows it is important to include everyone involved early when making AI tools for healthcare. Inclusive design means working with healthcare workers, patients, IT managers, and legal experts to find needs and risks before using AI.

• Engaging Users Upstream
  Involving users early in making products helps build AI tools that match real healthcare work and patient needs. For example, doctors and staff can say how virtual agents should handle hard situations or pass issues to humans.
• Transparency and Education
  Clear talk about what AI can and can’t do helps build trust. Training staff on how AI works, ethical rules, and privacy keeps AI use responsible and eases patient worries.
• Equitable Access
  Talking to diverse groups helps make AI tools that many kinds of patients can use. This includes people with disabilities, older adults, and those in rural areas. This helps reduce gaps in healthcare access in the U.S.
• Ethical Safeguards
  Working with legal and ethics experts helps create policies that protect patient control and privacy, keep people safe, and set clear responsibility rules.

Following these ideas helps healthcare groups avoid problems like making patients feel left out, breaking laws, or using unsafe AI tools.

Evidence from Research on AI and Immersive VR in Healthcare

A review looked at 132 studies from 2015 to 2025 about AI in extended reality (XR) healthcare simulations, including immersive VR. Most studies (62.1%) used VR and showed positive effects on improving healthcare workers’ knowledge and decisions. Two controlled trials found training with AI characters helped healthcare workers make better decisions and do tasks faster.

But the evidence was not strong because of small numbers of participants and big differences in studies. Often, quality checks like bias tests and transparency reports were missing. The review suggested a system called DASEX to check AI’s adaptivity, safety, and bias. These checks are key to keeping trust and clear responsibility.

Recent studies also say healthcare staff need more training on ethics, law, and privacy before using AI tools. Proper training and slow introduction help use these tools safely in healthcare.

Practical Considerations for U.S. Medical Practice Administrators, Owners, and IT Managers

Healthcare groups in the U.S. face special challenges when using AI virtual agents and VR:

• Compliance with Federal and State Laws
  Administrators must make sure AI tools follow HIPAA and other privacy laws. States have different rules for telehealth and licensing. Owners should work with legal experts to handle these rules when adopting technology.
• System Security and Monitoring
  IT managers should set up continuous checking to find wrong AI answers or malfunctions fast. Regular audits for bias and risk are needed to keep patients safe.
• Customized Training Programs
  Groups should create full training for staff on how to use AI tools, make ethical choices, and protect privacy. Training lowers wrong use and improves how staff explain AI to patients.
• Patient Involvement
  Practices should educate patients about AI virtual agents and VR tools, explaining what they do and their limits. Getting patient feedback helps improve AI tools and find problems or worries.
• Phased Implementation
  Research shows that introducing AI tools slowly along with staff training helps success. Rolling out tools step-by-step lets teams change workflows safely and fix problems before full use.

AI and Workflow Automation in Healthcare Front Offices

One important place to use AI virtual agents is in front-office work where many routine tasks take time from healthcare providers. AI automation can:

• Automate Appointment Scheduling and Reminders
  Virtual agents can schedule appointments alone, reducing phone calls and staff errors. Reminders help patients keep visits and reduce missed appointments.
• Answer Frequently Asked Questions
  AI can handle common questions about office hours, insurance, and needed documents. This frees staff for harder work.
• Triage Calls Efficiently
  AI can do first patient screening on the phone, deciding how urgent needs are and guiding callers to the right staff or emergency help.
• Support Billing and Registration
  Automated systems can help collect patient info correctly, fill out forms, and check insurance status.

These uses can save money and make work smoother but need careful watching to make sure AI answers are right, safe, and respect patient privacy. For example, mistakes in triage could cause serious health problems if they are not caught in time.

Addressing Ethical, Social, and Legal Dimensions

Research shows using AI in healthcare raises important ethical and social questions. For example, less in-person contact due to AI can reduce empathy and trust between doctors and patients. Healthcare workers need training not only on technology but also on ways to communicate better despite these changes.

Also, healthcare providers must balance AI help with human judgment. It is still unclear who is responsible if AI gives wrong advice. Clear rules need to be made with input from providers, lawmakers, and tech makers.

Making sure AI tools reach all groups fairly is very important in the U.S., where health differences exist. Developers and healthcare groups should focus on including underserved people so they benefit from AI rather than being left out.

Summary

Medical practice administrators, owners, and IT managers in the U.S. have an important job managing risks and duties when using AI virtual agents and immersive VR in healthcare. Using inclusive design and involving all users helps keep patients safe, meet legal rules, and make access fair. Adding good training, slow rollouts, and clear communication builds trust in AI’s role in healthcare teams.

Using AI in front-office work, like companies such as Simbo AI offer, shows how these tools can lighten workloads while following safety and privacy rules. But ongoing care, checks, and ethical attention are still needed as AI becomes more common in U.S. healthcare services.